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In this week's guest post,  Scott Blakeley shares perspectives on a growing trend in business - Terms Pushback (TPB). Scott is the founder of Blakeley, LLP, a noted expert in the field of creditors’ rights, commercial law, e-commerce and bankruptcy law. Scott regularly speaks to industry groups around the country and via monthly webcasts on the topics of creditors rights and bankruptcy. After a slow sales quarter, a large retail clothing store needs to improve their working capital and cash flow. Before the 2008 recession, the retailer likely would have turned to traditional business credit options. However, after the downturn, lenders changed their qualifications and terms, making traditional credit options a much less desirable option. In some cases, retailers, especially midsize or new businesses, can no longer even qualify for traditional credit sources. Businesses are now increasingly renegotiating their payment terms with suppliers through a program called Terms Pushback (TPB). When a jewelry retailer reviews its payment terms, for example, it sees that the main vendor supplier for jewelry currently requires payment 15 days after delivery. As a deliberate strategy — which is different from a company not having the money to pay its bill — to give the retailer more working capital, they reach out to ask the supplier to change the terms to 30 days. This means they would have access to the money paid to the vendor for 15 days longer; this process is often referred to as trade credit. Credit Today found that the most common extension is for 16 to 30 days, with 45% reporting this range as the most common extension. Impact of Terms Pushback on Suppliers While TPB improves cash flow for customers, it causes issues for suppliers because they must wait longer for their payment. Many consultants are now actively recommending TPB as a best practice. Because international companies more commonly using this strategy, many U.S.-based companies are adopting it due to international influence. Companies operating as middlemen between retailers and manufacturers often face the biggest challenges. Longer terms mean that they have less capital to buy more products and have a higher number of outstanding receivables. Additionally, businesses have lower cash conversion metrics, which can hurt publicly traded companies and cause concern for shareholders. According to Credit Today Bench-marking, 19% of suppliers always or usually say no to requests for extended terms, and 3% usually agree. Interestingly, 4% report that their answer depends on customer size — yes to large customers and no to small customers. However, the majority (63%) of businesses review the requests on their individual merits. However, denying the request often has long-term ramifications. If the business denies the request, the customer must pay or suffer credit damage. Customers often get around this by paying late enough to improve their own cash flow but before credit damage occurs. Even more challenging, suppliers are often hesitant to report customers to credit bureaus because this often permanently damages or even ends the relationship. If a supplier denies the request, the majority of options to get the payment are punitive. For example, the supplier can charge a late fee for payment. However, the customer may still decide that the value of the money for the extra days is worth more than the late fee. Other avenues include implementing a credit hold, having two price lists, terminating credit, firing the customer and reporting the customer to industry groups. However, each of these options permanently damages and probably ends the customer relationship, which may result in loss of a high volume of sales. Effectively Managing Terms Pushback with Supply Chain Finance Programs Supply Chain Finance programs are asset-based lending programs structured to improve a customer’s payment terms, reduce costs and improve cash flow enabling financial institutions to pay suppliers for invoiced services. Suppliers can benefit from SCFP as it receives payment within normal terms or earlier which helps keep the credit team’s credit scoring and risk models consistent. The customers benefits as well as their capital is not tied up in day-to-day operational payments and creates more reinvestment opportunities. When a company receives a TPB request, the first step is evaluating the customer — their credit, the risk, the volume of business and the value of the relationship for the supplier. Often, larger companies have an advantage over smaller companies when negotiating term extensions because their business relationship is worth more to the customer than the monetary value of the shorter term. Here are three best practices to managing TPB requests: Offer incentives for shorter terms. Instead of punitive actions, consider giving customers who pay within shorter terms a discount or an annual volume discount for consistent payment with shorter terms. Actively monitor threshold for customers with extended terms. Suppliers must effectively manage their own cash flow to make payroll and other expenses. By extending too many customer terms, suppliers can jeopardize their own financial stability. Create a team to evaluate requests. By establishing a process to handle the requests and a team to formally evaluate requests, suppliers can more effectively evaluate all aspects of the decision, such as the risks of extending to its own financial health and the risk of losing the client relationship. With a team made representing stakeholders from different departments, all perspectives can be represented and considered. As TPB becomes more common as a strategy, suppliers must proactively create a process to manage requests. Often, extending the terms can improve the customer relationship and even increase the amount ultimately paid. By creating a team and strategy, suppliers can make the smartest decision and actively manage pushback requests. Scott Blakeley is a founder of Blakeley LLP, where he advises companies around the United States and Canada regarding creditors’ rights, commercial law, e-commerce and bankruptcy law. He was selected as one of the 50 most influential people in commercial credit by Credit Today. He is contributing editor for NACM’s Credit Manual of Commercial Law, contributing editor for American Bankruptcy Institute’s Manual of Reclamation Laws, and author of A History of Bankruptcy Preference Law, published by ABI. Credit Research Foundation has published his manuals entitled The Credit Professional’s Guide to Bankruptcy, Serving On A Creditors’ Committee and Commencing An Involuntary Bankruptcy Petition. Scott has published dozens of articles and manuals in the area of creditors’ rights, commercial law, e-commerce and bankruptcy in such publications as Business Credit, Managing Credit, Receivables & Collections, Norton’s Bankruptcy Review and the Practicing Law Institute, and speaks frequently to credit industry groups regarding these topics throughout the country. He is a member on the board of editors for the California Bankruptcy Journal, and is co-chair of the sub-committee of unsecured creditors’ Committee of the ABI. Scott holds an B.S. from Pepperdine University, an M.B.A. from Loyola University and a law degree from Southwestern University. He served as law clerk to Bankruptcy Judge John J. Wilson. He is admitted to the Bar of California.

Serving commercial Property & Casualty insurers is a major objective of 3rd parties in the analytics and data space. This industry vertical is one in which standard credit tools already apply to the carrier’s challenge in managing claims risk; there is continued investment within and beyond the industry in developing innovative tools for this purpose. However, a smooth roll out of such tools at scale requires a comprehensive understanding of the regulatory process and its constraints. US Insurance industry- overall regulatory structure: Currently, US carriers are regulated primarily by the individual states, a result of the 1945 McCarran Ferguson Act (“MFA”). Less known is that the MFA was presaged by the Paul v Virginia decision (1869, later overturned by SCOTUS) that held that issuing an insurance policy was not a commercial transaction! [1]. Federal regulatory guidance, ultimately from the Office of the Controller of the Currency (OCC) and the Federal Reserve Board (FRB), is implemented via the National Association of Insurance Commissioners (“NAIC”; see below). NAIC organizes the insurance commissioners from all 50 states, Washington DC, and territories. NAIC maintains legislative databases, market conduct standards, industry financial reporting, conducts training, and many other functions. NAIC provides supervisory guidance for the use of models used to predict insurance loss risk. Among other functions, NAIC has created the Own Risk and Solvency Assessment (“ORSA”) framework which implements existing OCC and FRB guidance to the states. Capital reserves needed for solvency as well as business conduct -- including product definition and general business operations, licensing, maintaining a guaranty fund, underwriting, and rate setting-- are determined primarily by the states in which the carrier operates [2]. Today’s system of state-by-state regulation is more challenging than an equivalent centralized regulating body; insurance carriers operate increasingly online, driving the need for multi-state operations which in turn require multistate licensing and complex regulatory compliance. The average property liability firm has 16 state licenses, while the average life insurance carrier has 25. The coordination of state insurance laws, as well as many other quasi-governmental insurance industry functions, falls under the aegis of the NAIC. We will focus our discussion here on the regulation of risk models. How should third parties align the model building with regulatory requirements? Example 1: Basic filing and disclosure protocol: Responsibility to disclose to state regulators typically lies with the developer or the owner of the model. Disclosure responsibility for custom risk models built around the data of a specific client insurer resides with the insurer, while industry standard models used for multiple clients are typically disclosed by the model developer. Reporting and disclosure requirements vary by state. While the most central functions of interest by state regulators are underwriting and rate setting, any other use of models by insurers may be subject to regulatory disclosure. Models used to assess loss risk for rate setting or underwriting purposes are typically examined for discriminatory impact and use of prohibited data in addition to adequate risk performance and numerical stability. “Prohibited data” varies by state but may include certain data elements gleaned from in-state residents, federal crime data, certain credit data elements, traffic violations exceeding a specified age on the books, or other data; the section below deals with credit data. Finally, the requirement to disclose model details such as attributes and weightings also vary between states, and may require the developer to invoke trade secret status for the subject models to avoid disclosure to the public (implicit in many states). The adjudication of such claims is variable between states, as are all communications with regulators on this topic. Example 2: Use of consumer credit information to underwrite personal insurance policies: Using credit information in models to predict loss risk on personal insurance contracts also has a rich and extremely active history in the US. P&C insurers have generally found that credit risk and claims risk are positively correlated. They have used credit data on individual consumers to various degrees. Notably, the Consumer- Based Insurance Score (CBIS) employs consumer credit parameters and has been used across the insurance industry since 1993. Amid vigorous debate, states have seen active legislative attempts to restrict and define allowable use of consumer credit data by insurers. Credit information in some cases can outweigh a consumer’s driving record in setting rates- leading to the bitter but factual observation that excellent consumer credit can literally outweigh a DUI conviction in some states and conditions. In 2016 alone, the state legislative actions below were considered and/or enacted; note once again that the ability of individual states to regulate independently greatly complicates the picture for large carriers operating in multiple states:  California, Hawaii, and Massachusetts do not appear in the table above. In those states, consumer credit information cannot be used to underwrite personal auto policies. Example 3: Reporting channel: State regulators typically require use of the System for Electronic Rate and Form Filing (“SERFF”) database maintained by NAIC for formal submissions: https://login.serff.com/serff/ What’s coming down the road? We have seen examples of the dependence of applicable insurance regulations on individual state laws; the mechanics of model development requires understanding and working with these restrictions. Basic filing and disclosure, permissible model variables, the proprietary status of model detail, and the use of certain consumer information (e.g., credit scores, driving records) are all aspects of risk models whose successful execution depends on understanding the widely variable set of existing state regulations. Several authors have cited the need for a shift in the underlying regulatory structure of the industry from state-based to a national system, citing the inefficiency of the licensing process and the true interstate nature of today’s distribution system. A centralized federal insurance regulatory body would simplify interstate compliance by carriers, but would also introduce other complications. However, it appears prudent in the near-term for 3rd parties developing models to gain awareness of, and streamline, current requirements for regulatory compliance at the state level. Conclusion: There is a considerable additional value that the next generation of models will contribute to the commercial P&C vertical. Insurers and 3rd party developers have demonstrated the applicability of their models and data reports, offering competitive added value with standard risk scores adapted from the credit domain. However, promoting these products more broadly and expanding the product offerings themselves into cyber risk, commercial linkages, and various other tools for insurers, the insurance industry faces efficiency hurdles from our 50-state regulatory framework. With any regulatory centralization unlikely near term, 3rd parties thus need to gain working fluency in NAIC and in the SERFF database, anticipate state-level documentation and disclosure requirements, and attain a level of familiarity with state regulatory machines that enables the management of the interests of their clients with confidence. How Experian can help you Experian provides analytical services for Property & Casualty as well as other insurance product verticals. To enable you to assess claims risk at the time of policy application (or renewal), we either apply standard risk models or develop custom risk models to your underwriting and rate-setting processes. To help you guard against cyber fraud, false identity, and reputation risk, we offer specialty products as well. We also offer special purpose, custom analyses on request, and we sell curated commercial data to your standards as well. References: [1] Brookings Institute. paper on future of regulation- Grace & Klein [2] Insurance Information Institute: Regulation [3] Grant Thornton: ORSA requirements: Model Risk Management for Insurance Companies [4] Blueprint for a Modernized Financial Regulatory Structure, Dept. of Treas., 2008  

Today we are very proud to be taking the wrapper off the next generation of our flagship commercial credit management application, BusinessIQSM 2.0. To meet the ever-changing needs of our clients, we continue to grow and modernize with them.  This innovative and powerful analytical web-based application is designed for commercial enterprise and small-business risk management. From the new interface and side bar navigation to enhanced search and match technology, to judgmental and rules-based scorecards, all the way to custom model scores, Experian’s BusinessIQ 2.0 has something for everyone. Let Experian meet you where you are and take you to where you want to be. BusinessIQ 2.0 Overview In this video we highlight some of the key features of BusinessIQ 2.0. Learn more by going to:  

I have been on the road meeting with clients at advisory events, forums, and industry thought leadership conferences, and what I continue to hear is a concern about the upcoming recession. The drivers of the next recession are up for debate but the consensus is that it is inevitable. The U.S. Economy is complex and the signals are mixed as to where the greatest impact will be felt. Protecting your business, whether consumer or commercial focused, is dependent on the stability and strength of your lending criteria and customer engagement practices. You want to protect your customers as well as your business in the case of a market stumble. You are laser-focused on making the best possible decision when reviewing credit applications and setting loan terms, however, financial situations change over time for both individuals and companies. This is especially true when a recession hits and unemployment begins to rise, consumers stop spending, and commercial delinquencies begin to rise. When these macroeconomic changes occur, the credit you have extended to your portfolio might be at under market stresses and at a stronger risk of nonpayment, and this can affect your business’s health and sustainability. By stress testing your portfolio, you can determine what may happen, when stresses are exerted, by a receding economy, on your portfolio. You can use credit information, macroeconomic data, and alternative data to build models that forecast what is likely to happen in the future and how stresses, will affect the ability for people or businesses to pay their bills. While larger regulated companies may be required to perform forecasting and stress testing, lenders of all size can benefit from the process. Gathering the Right Data for Accurate Stress Testing The accuracy of your stress test depends on the type and quality of data used for forecasting. Recessions are cyclical and likely to re-occur every few years, it is recommended that companies use historical data from the 2008 recession for analysis and to make accurate predictions. Young businesses may not have complete historical data going back to the 2008 recessionary time period. A partner like Experian can create look-alike business samples, from the vast holistic data, to simulate the likely impact of macroeconomic scenarios. For example, a financial services firm has been providing small business loans between $50,000 and $100,000 for the past three years and wants to predict future losses. To gather the data for loss forecasting, you need to create a business and product profile identifying loans or businesses with similar characteristics, to stress and forecast performance. These profiles are used to build a look-alike sample of businesses and loan products that look and perform like your current portfolio and will add the sample size and retro time periods needed to create a statistically viable analysis sample. Selecting a Forecasting Strategy Once you have the historic credit, macroeconomic, and alternative data on your portfolio or look-alike retro sample for modeling, you need to stress test the data. Most stress test analyses start with a vintage based analysis. This type of analysis looks at the performance of a portfolio across different time periods (Example: March 2007, March 2008, March 2009, etc..) to evaluate the change in performance and the level of impact environmental stresses have on the portfolio's performance. Once you have this high-level performance, you can extrapolate into the future performance of the portfolio and set capitalization strategies and lending policies. Identifying Loss Forecasting Outcomes Regulators and investors want to know the business is solvent and healthy. Loss forecasting demonstrates that your company is thoughtful in its business processes and planning for future stresses. For regional lenders that are not regulated as closely as large national or global lenders, forecasting shows investors that they are following the same rules as larger regulated lenders, which strengthens investor confidence. It also demonstrates effective management of capital adequacy and puts you on a level playing field with larger lenders. Companies with limited data can start with credit data for look-alike sample development and add historical data and alternative type data as they grow for a holistic portfolio view. Setting up Governance Business policies and macroeconomic stresses change over time, it’s essential to set up a governance schedule to review forecasting processes and documentation. Your stress testing and forecasting will not be accurate if you design it once and do not update it. Most companies use an annual schedule, but others review more frequency because of specific circumstances. Effectively Documenting Loss Forecasting The key element of loss forecasting is effectively documenting both sample and strategy taken in the evaluation of your portfolio. A scenario you might face is when a regulator looks at the analysis performed and you have selected sample data at the business level instead of the loan level, documentation should capture the explanation of why you made the decision and the understood impacts of that decision. While the goal is to have complete data, many companies do not have access to high-quality data. Instead of foregoing loss forecasting, the use of documentation to note the gaps and build a road-map for the data can be of great value. Here are additional key points to include in the documentation: • Data sources • Product names • Credit policies • Analysis strategy • Result summary • Road-map and governance schedule By creating a stress-test analysis strategy for forecasting loss, your company can make sure its portfolio and financial status remain as healthy tomorrow as they are today while maintaining transparency and investor confidence. The next recession is out there, this is a great time to strengthen processes for future successes.  

It's International Fraud Awareness Week and Experian would like you to know how big the problem is for businesses. Here are some sobering facts, did you know? Every year 3.7 trillion dollars is lost to fraud? it would take the average person to spend 130 million dollars per day in their lifespan to cover that amount.     Fifty four percent of businesses are only "somewhat confident" in their ability to detect fraudulent activity. And that's compared to only 40 percent who are very confident.  52 percent of businesses have chosen to prioritize the user experience over detecting and mitigating fraud. Organizations worldwide lose an estimated 5 percent of their annual revenues to fraud, and an incident of fraud costs a company a median loss of $145,000. .  

Experian Business Information Services recently introduced a powerful new marketing platform called Business TargetIQ. Product Manager, Kelly DeBoer answered a few questions about the product and described use cases that promote greater collaboration between credit and marketing departments. What does Business TargetIQ do? Business TargetIQ is our new marketing platform so it's a B2B marketing platform where clients can access data for marketing applications. How is it different from other business marketing platforms? It is unique in that it not only includes your standard or core firmagraphic information but also includes Experian's credit attributes. Does it have credit data? What does that mean to marketing or collaboration? Typically marketing data and credit data are housed in separate silos of information. With this tool the information will be combined together which will allow the tool not only to be used in traditional marketing applications for targeting but can also be in that risk factor which applies to different divisions within our client's applications or use cases of the data. Who would most benefit from Business TargetIQ? The thing about Business TargetIQ is it truly applies to all different verticals, as well as all different contacts within the company. So whether it's a financial vertical or a trade vertical, retail, just across the board all clients can utilize this. Anybody that's doing marketing can utilize this platform. What core problems does Business TargetIQ solve? It solves a lot of different problems, so, the most common client issues that are brought to our attention are gaps in data, as well as in the marketing initiatives. So they may have data in-house but they have holes within the data. Our tool will allow them to not only upload their client records and fill in a lot of those gaps that they may have, whether it be contact information, or firmagraphics or address information. It will standardize that data and fill in those gaps. But will also provide the means to again use that data. Our business database which has over 16 million records. They can then utilize that information for prospecting, for data append, for analytics, for research applications, so it solves a lot of problems with regard to marketing and data concerns. How does credit data help with prospecting? So what we find is clients come to us and they may say you know I have an idea of what our clients look like, they're in this SIC or in this industry code, or they have this sales volume or employee size, but what they may not know is on the back end which really helps identify and target those businesses is the credit attributes, so the risk factors around those. So do they have delinquencies in their payments? Have they filed bankruptcies? Do they have UCC filings? So it allows them to take it that next step and not only really define what their clients look like, but identify clients that look like that. Learn More About Business TargetIQ

As a Senior Consultant with Experian Advisory Services, Gavin Harding works closely with many of Experian's FinTech and Financial Institution clients to find solutions to complex problems. We sat down recently to talk about bank partnerships, how they come about, what makes them successful, and how Experian supports them. Do you see a lot of collaboration between banks and FinTechs? The latest statistics show that 67 percent of banks and FinTech’s are either currently cooperating, or in discussions about cooperating, or exploring collaboration. So, yes a very significant proportion are considering collaborations. Why collaborate at all? You know it's interesting, they have different skill sets, different assets, different backgrounds. So for example; banks have really deep, broad customer relationships. You know think about your Mom or Dad bringing you to your local bank to open up your first account. Think about your student loan. Think about your mortgage. What kinds of relationships exist? So banks have really deep and broad relationships. But traditionally the experience with banks has not necessarily been great in terms of turnaround, in terms of the friction or pain involved in getting a loan or opening an account. On the other side, FinTech’s are really good at that customer experience. They describe it as either low or no friction. So very quick turnaround times. But they're very much transactional-focused, meaning single products. So FinTech has the technology and the experience, and banks have the depth of relationships with customers. You bring those two parts together and you've got a pretty amazing potential opportunity.  There are as many relationship types shapes and sizes as there are people on the planet. Everything from cooperation on basic operations, meaning, a FinTech takes applications for a bank and then passes them on. All the way over to full-fledged integration of systems, personnel, capabilities, skill sets, and so on. So pretty much the broad spectrum. What works well? So it works really well when they are well-matched. So what I mean by that is, when the skill sets from one organization match the other. When one enhances the other, and it works really well when there are long and detailed discussions and preparations for the relationship. Meaning, they align and discuss goals, objectives, what each organization's role is, what each brings to the table, and very specifically how they are going to cooperate. What are the pitfalls? Well, the same pitfalls. So the pitfalls are that the relationship goals differ, or aren't aligned, or that one organization feels like they are bringing more to the relationship and that the partnership is equal, or when it feels as if each partner, each organization is not getting value from the relationship over time, and once again that reinforces the need for those detailed discussions before getting into that partnership or relationship. How does the process work? So it begins with a discussion. I've seen these partnerships start with a discussion over dinner at a conference. I've seen them start through a LinkedIn connection. I've seen them start over coffee. So it really starts with an exploration of who's out there? What organizations may be interested in even discussing some kind of collaboration? So it starts with the conversation at the very basic level, even when we see in the Wall Street Journal major strategic alliances between organizations, starts with people, and starts with that very simple conversation and connection. What are some key elements to be aware of? Well again it comes down to what each party brings to the relationship and what the goals are. So a good alignment of the capacity of skill sets, an alignment of investment in terms of time and resources, and very specifically a definition of who does what, what the accountabilities are, and what everybody's expectations are. They are fundamental to the success of any type of business arrangement or partnership. How does Experian support these partnerships? So the interesting thing is we have very deep relationships with both sides. So we bring data, solutions, consulting expertise to FinTech’s and to banks. So, it's really interesting we find ourselves in the middle of a lot of these conversations, and how we help is by understanding systems, technology, data, the best of both organizations involved in the conversations, and how to bring all of that together for a good focused efficient successful outcome. A couple of years ago this was new meaning that banks saw FinTech’s growing, and kind of looked at them a little bit maybe as competition, as potentially the enemy, FinTech’s saw themselves as disrupting the world and completely innovative and new. What's starting to happen is both sides are coming together, realizing that they are both part of the same financial industry, serving the same customers, maybe in different or new ways with different products. But in the same industry. So there is very much a coming together, an alignment a co-mingling, consolidation of all these various aspects of the industry. And I think it's really positive for consumers. More products, more quickly, and a better experience overall. Do you think a FinTech's ability to create more dynamic mobile experiences is a key element Certainly and so the big question we help banks answer in this space is, do we build it? Do we buy it? Do we partner? and build and buy or partner refers to the technology the infrastructure and the experience. So if you have a pretty big bank and they've got a old website, old process, lots of paper, lots of regulations, lots of pain in the process. Well they can look at one of the more advanced sophisticated mature FinTech’s and essentially use their platform, their engagement, their data, connect that to the bank's customers and in a very very short time transform that experience in a very positive way for their banking customers.   Learn about FinTech Lending Solutions

We sat down with John Krickus, Senior Product Manager for Experian's Scoring solutions to ask about the new Social Media Insight, and how this data and score are being used to help businesses strengthen scoring models, and create opportunities for emerging small businesses with limited credit history but strong social media profiles.  What is Social Media Insight? We're very excited to bring what we consider a breakthrough capability. Social Media Insight is an expansion, a use of new information beyond traditional credit data, that both improves the performance of our scores, provides attributes to our clients, and is directly sourced from the social media providers - no screen scraping. What type of social media data is used? So the social media data comes from sources that, like with our other data sources, we are not allowed to publicly disclose, but we are focused on business social media sites. So we're not going after consumer social media data, but social media data from business-oriented sites, so that we can better evaluate small and midsize businesses. What steps are taken to prevent artificially boosting the ratings of a business? So that's a question that we often receive about artificially boosting ratings. We work with social media companies, they have many techniques to identify where the reviews are coming from and to prevent someone gaming the system. Is it 100 percent full proof? No, but it is very effective. How effective has social media data been in predicting risk? As we've seen with using the data in our scoring solutions, we've seen a tremendous boost in score performance. There have been two real gains from using social media data. First is, we've developed about 70 social media attributes. We can now include those attributes and make these attributes available to our clients to improve the performance of risk scores. The second area really devolved from client feedback as we piloted the data. They indicated to us that there were social media data elements that are very helpful. So, we've been able to attain information such as pricing, parking situation, hours of operation, and those additional data elements have also helped our clients in improving their risk performance scores. What type of data do our clients get with Social Media Insight? We're able to provide our clients one of three types of data. First, we can provide a social media IntelliScore. That's our normal IntelliScore with commercial data, and now social media data. So, you get a higher performance. We've boosted performance by 37 percent with our IntelliScore. Second, we can provide those same social media attributes to our clients. So, they can incorporate those social media attributes into their scoring models. And third, we have the descriptive data. I mentioned hours of operation for example, social media data also provides a better description of the business. So, you just don't get an Exercise Gym, you get whether that gym is kickboxing or whether that's just exercise equipment. Can I target specific kinds of businesses? Can it be used for Marketing? We do have the ability to use social media data for identifying better businesses to do business with. Our initial focus though is in developing the attributes and the score for risk management. So that's really the focus for this first phase of Social Media Insight. How might Social Media Insight help an Insurance company? Insurance companies have been very anxious for this data, and they're getting a different view of the business. We're going beyond traditional trade, public record, background information, and now we're able to provide a view of how long has that business been rated? How are the ratings? What's the trend in the number of ratings? So, it's not just your level rating, but are you getting reviews over time? All that information provides a really unique view of the business that we've never seen before. How can Experian clients access Social Media Insight? Our clients can access the data one of two ways. We can provide a batch file. So, if you have a portfolio and you want to add social media attributes and a social media score to that you can provide us a file in batch. We also have our new API access, and we're very excited about that. So that allows you online real-time access to obtain the social media insight data, and it's very easy to program to. Can Social Media Insight help emerging businesses gain access to capital? Yeah that's an excellent area for Social Media Insight. It's really the newer smaller businesses that don't have a very deep credit history, that Social Media Insight now provides a view that previously it may have lacked information, therefore, credit may not have been extended. Now by having a social media site data available. We're adding depth of information to that business. We've actually found that businesses with social media data, as a group, are less risky than businesses without. How does Social Media Insight help improve risk model performance? So, the view that the social media data provides, we have found has boosted model performance, more than doubling model performance for those new emerging businesses. But even for established businesses we've seen double-digit gains in the measure of performance for models such as KS, and again you're getting a view of a business, number of reviews, and we normalized that. So, if you're in an area that's very active with social media data we take that into account. So, if you have 10 reviews in your history, which are in a very active area, you may not get as much of a positive as if you had 10 reviews and a less active social media area. So that combination really boosts performance and predictiveness of the data. How do Social Media Insight help our clients reduce risk? And really that's been one of the biggest breakthroughs with the social media data. Is by being able to boost the performance as I discussed earlier on KS, those new to the world businesses. They now are able to more confidently make decisions in their portfolio, because there is now a wealth of information. They're able to improve their models with this new additional information and have a very good performance improvement with it before and after. So, there's an across the board performance improvement. How does Social Media Insight help to automate the decisioning process? When you have an automated system, you want to have a higher level of confidence. The higher level of confidence you have, the stronger you can, for example, having automatic approve and automatic reject areas. By adding social media data we're able to get a stronger KS performance, which means you have more confidence in the models, and you can now increase the percentage your portfolios that you're putting through either automated or highly automated decision making. It's a significant boost in performance however you cut it. We're very excited about this unique new information source. Social media data is totally different from any other business credit information that's available, and when it's utilized in a model, in a decisioning system, the gain in performance are dramatic, and we're very excited to bring this capability to our clients. Learn about Social Media Insight  

Are the credit models you are using to make lending decisions more than 2 or 3 years old? If so, you are likely making less than optimal credit decisions. You may be turning down a customer who is a good risk — while taking on customers who are more apt to default on their obligations. Every year a model isn’t updated, its accuracy decreases. The economy changes. The consumer’s or business’s financial situation changes. Updating your models, using the most current data and attributes available, you can have confidence that you are making good credit decisions. To make the most accurate credit decisions possible, many businesses are now turning to data-driven decisioning models that are powered by artificial intelligence (AI) within machine learning engines. While the standard regression model works well in some industries, the lift in predictive value from using AI data models can be very important in other industries, such as retail, fraud and marketing. These models use sophisticated algorithms to predict the customer’s future ability to repay their obligation, which means a much more accurate decision than traditional models. Starting with High Quality Data   While data has always been at the core of credit decisions, models using machine learning are even more dependent on data. These models can be very accurate, but their accuracy depends on having the necessary data to understand what happened in the past and present behavior to make a prediction for what will happen in the future. The more data provided, the higher the accuracy of the decision. Here are three things to consider when building your data-driven decisioning model:   Clean Data – As innovation spurs business and technology to run faster and more efficient, the quality of the data underneath all of that innovation becomes even more important. Machine learning becomes smarter the more data it consumes. This means the accuracy of the credit decisions made by the model is largely dependent on the quality of the data provided. Data from third-party sources often contains mistakes, missing fields, and duplicate information, which results in less accurate credit decisions. Correct Data Points – The accuracy of the results depends on considering the right criteria in the form of data points in the model. When you use machine learning and AI algorithms, they can predict which specific data points will help increase the performance of the model for the specific customer and the specific type of credit decision. Often, data points that you may not consider are the ones that can make a big impact on the accuracy of the decision. Real-Time Data – In the past, there was often significant lag time between collecting and being able to use the data. By using real-time data with machine learning models, you can get a clear picture of the most current view possible and see changes in the different data points as they occur. This lets you make a much more accurate prediction of what will happen, with the consumer or business, than was previously possible with a traditional credit decisioning process. Using Alternative Data to Get the Full Picture Often, additional data — typically referred to as alternative data — that is not readily available from traditional data providers is used to enhance the accuracy and predictive ability of a model. While the model can seem complete without this information, the model may provide suboptimal results without it. Machine learning models can predict the situations and exact type of alternative data a model needs to produce an accurate decision. Experian offers a wide variety of alternative data that clients can use to improve decision models.   For example, a business owner may be taking out short-term loans to increase her cash flow, which makes her a much higher credit risk than she appears to be without this data. Weather information is also a common type of alternative data; a business located in Tornado Alley may need higher cash reserves to be a good credit risk. On the other hand, businesses located in an area impacted by a recent weather event, such as a hurricane, may be a good credit risk even with a lower score because both their business and local economy is recovering. Regularly Evaluating Your Data Model You must build in governance and make sure you are evaluating how the model is working on a regular basis, like having an annual checkup with your healthcare providers. Once you begin using a data model, you can’t simply set it and forget it. Ask the following questions to periodically evaluate your models:   Are there changes in the outcome of the models? You need to verify that your attributes are still predicting the same outcomes as intended, as well as capturing the same data. For example, say you have an attribute in your model that counts the number of credit lines open for a small business. If the attribute changes and those types of credit lines are no longer reported by the data provider, that number can go from three or four to zero, without there being a change in the number of credit lines open by the business. Because the data that goes into your model has changed, your model is not accurate unless you update the attribute. Is your model stable? You need to make sure that degradation hasn’t reached a point where the predictive value is no longer accurate. For example, scores before the 2008 recession have a different meaning than afterward, due to the changes in the financial system. The future of your business depends on making accurate credit decisions. Instead of using outdated models, use the latest technology and methods available by using machine learning data-driven models. It’s simple. It’s quick. And most importantly, data-driven models are accurate. Related articles: How To Modernize Decisioning with Automation and Real Time Respones Integrating Credit Decisions with the Back Office Improving Customer Experience Through Decisioning as a Service (DaaS)